Cover Image

Relative Permittivity of Carbon Dioxide + Ethanol Mixtures prediction by means of Artificial Neural Networks

Gonzalo Astray, Manuel A. Iglesias-Otero, Jorge Morales, Juan C. Mejuto

Abstract


CO2 + ethanol mixtures have a huge scientific interest and enormous relevance for many industrial processes. Obtaining of their chemical and physical properties is a fundamental task. Relative permittivity (ï¥r) of these mixtures is a key property because allows a better knowledge of the structure and the interactions in other media. In this work predictive values of relative permittivity (ï¥r) of carbon dioxide + ethanol mixtures were obtained implementing artificial neural networks (ANNs). They are used successfully in very different fields; therefore it is a very useful tool. In this case the obtained results enhance the ones from the usual multiple linear regression analysis. In both cases mass fraction, pressure and temperature experimental data from a direct capacitance method were used.


Full Text:

PDF

References


F. S. Oliveira, M.G. Freire, P.J. Carvalho, J.A.P. Coutinho, J.N. Canongia-Lopes, L.P.N. Rebelo, I.M. Marrucho. J. Chem. Eng. Data, 2010, 55, 4514-4520.

B. González, E. J. González, I. Domínguez, A. Domínguez. Phys. Chem. Liq., 2010, 48, 514-533.

B. Mokhtarani, A. Sharifi, H. R. Mortaheb, M. Mirzae, M. Mafi, F. Sadeghian, F. J. Chem. Thermodynamics, 2009, 41, 323-329.

F. X. Feitosa, M. L. Rodrigues, C.B. Veloso, L. CeLio, L.J. Cavalcante, M.C.G. Albuquerque, H. B. De Sant’Ana. J. Chem. Eng. Data, 2010, 55, 3909-3914.

L.A. Blanchard, J.F. Brennecke. Ind. Eng. Chem. Res., 2010, 40, 287-292.

J.L. Kendall, D.A. Canelas, J.L. Young, J.M. DeSimone. Chem. Rev., 1999, 99, 543-563.

W. Leitner. Acc. Chem. Res., 2002, 35, 746-756.

L. Teberikler, S. Koseoglu, A. Akgerman. J. Am. Oil Chem. Soc., 2001, 78, 115-120.

J. Ke, C.Mao, M. Zhong, B. Han, H. Yan. J. Supercrit. Fluid., 1996, 9, 82-87.

S.S.T. Ting, D. L. Tomasko, N.R. Foster, S. J. Macnaughton. Ind. Eng. Chem. Res., 1993, 32, 1471-1481.

M. D. Saldaña, C. Zetzl, R. S. Mohamed, G. Brunner. J. Agric. Food Chem., 2002, 50, 4820-4826.

T. Baysal, S. Ersus, D. A. J. Starmans. J. Agric. Food Chem., 2000, 48, 5507-5511.

O. Teschke, G. Ceotto, E.F. De Souza.. Phys. Chem. Chem. Phys., 2001, 3, 3761-3768.

G. C. Franchini, A. Marchetti, M. Tagliazucchi, L. Tassi, G. Tosi. J. Chem. Soc. Faraday Trans., 1991, 87, 2583-2588.

C. J. Chang, C. Y. Day, C. M. Ko, K. L. Chiu. Fluid Phase Equilibr., 1997, 131, 243-258.

Z. Sun, X. Zhang, B. Han, Y. Wu, G. An, Z. Liu, S. Miao, Z. Miao. Carbon 2007, 45, 2589–2596.

B. S. Chun, T. Gordon, T. Wilkinson. Ind. Eng. Chem. Res., 1995, 34, 4371-4377.

W. Eltringham. J. Chem. Eng. Data, 2011, 56, 3363-3366.

L. Jared, J. D. Anderson, T. Welton. D. W. Armstrong. J. Am. Chem. Soc., 2002, 124, 14247-14254.

S. Haykin. Neural Networks: A comprehensive foundation; Prentice Hall: New Jersey, 2008.

J. R. Hilera, V.J. Martíne. Redes neuronales artificiales. Fundamentos, modelos y aplicaciones; Addisson-Wesley Iberoamericana S.A.: Madrid, 1995.

S. Haykin. Neural Networks and Learning Machines; Pearson Prentice Hall: New Jersey, 2009.

G. Astray, P. V. Caderno, J. A. Ferreiro-Lage, J. F. Galvez, J. C. Mejuto. J. Chem. Eng. Data 2010, 55, 3542-3547.

R. W. Janson, C. Batur, L. Krishna. Ohio J. Sci., 2001, 101, 57-64.

G. Astray, F. J. Rodríguez-Rajo, J.A. Ferreiro-Lage, J.A. Fernández-González, M.V. Jato, J.C. Mejuto. J. Environ. Monitoring, 2010, 12, 2145-2152.

G. Astray, J. X. Castillo, J.A. Ferreiro-Lage, J.F. Galvez, J.C. Mejuto. Cienc. Tecnol. Aliment., 2010, 8, 79.

H.R. Maier, G.C. Dandy. Environ. Mod. Soft. 2000, 15, 101-124.

P. Araujo, G. Astray, A. Cid, A. Orosa, O. Moldes, B. Soto, J.A. Rodríguez-Suarez. Electron. J. Environ. Agric. Food Chem., 2011, 10, 1608-1615.

A. Habibi-Yangjeh. Phys. Chem. Liq, 2007, 4, 471-478.

I. Rivals, L. Personnaz. IEEE Trans. Neural Networks, 2000, 24, 9-10.




DOI: http://dx.doi.org/10.13171/mjc.2.1.2012.10.09.09

Copyright (c) 2015 Mediterranean Journal of Chemistry